java-face-detection

An implementation in java of the viola jones algorithm using a trained classifier

Pre-requisites

This requires

• Java
  • Set your Java JDK to 1.8
• Using an IDE is recommended due to the complexity of configuring openCV
  • IntelliJ IDEA, Download
• OpenCV 3.1, Download
  • A compiled mac jar is provided here lib/openCV/mac/opencv-310.jar
  • You need to configure openCV 3.1 and add it to to the project class path.
  • Configuration steps for Mac OS X can be found here
    • After 'make' has successfully executed, navigate to that directory and locate the 'lib' folder
    • Find 'libopencv_java310.os' and change this file extension to '.dylib'
    • Add 'opencv-301.jar' to your build path. This jar file can be found in lib/openCV/mac/ in this repo
    • Configure the module library to add the lib folder of the openCV you just compiled
• Java-json (included in repo)
  • locate 'java-json.jar' and add it to your build path

There are two parts to the face detection

Training Classifier

this can be done by

• Calling CascadeClassifier.train() test. This implementation does not yield good results
• PREFERRED Using the python trainer here (same creators)

Face Selection

• The python face detection on single image
• PREFERRED Using java MainUI.main() which gives you the following functionalities

Integral image visualization

Calculate Integral Image and display it

Method 1 - Using base features

• Create 100 feature vectors from 100 images stored in res/baseFeaturesTrainingSet/faces.
• Use cosine similarity to compare each test image feature vector Tx to input image feature vector I
• Use similarity threshold sT to determine if that comparison yields a face.
• Find the average of faces / Sum(faces+nonFaces) and use the final threshold ft to determine if the input image is a face

Method 2 - Using adaboosted 1 stage classifier

• Train a one stage classifier using the face images in res/trainingSet/faces and the non face images in res/trainingSet/nonFaces and the 1000 Haar features (of the > 160k haar features in a 24x24 window)
• Use that one stage classifier to determine whether the input image is a face or non face

Method 3 - Using a cascaded classifier

• Import your own cascaded classifier. You can train the classifier as explained in Training Classifier section above.
• If you don't import a cascaded classifier, the default one will be used. The default is the best classifier we were able to train so far.

Method 4.1 - On loaded image

• You can detect faces on the loaded image

Method 4.2 - Directly from webcam

• You can directly use your webcam to detect faces